{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Normalization\n",
    "\n",
    "\n",
    "Mean normalization involves centering the variable at zero, and re-scaling to the value range. The procedure involves subtracting the mean of each observation and then dividing by difference between the minimum and maximum value:\n",
    "\n",
    "**x_scaled = (x - x_mean) / ( x_max - x_min)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# the scalers - for mean normalisation\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# Remove 2 variables:\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 6), (6192, 6))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc           3.866667\n",
       "HouseAge        28.618702\n",
       "AveRooms         5.423404\n",
       "AveBedrms        1.094775\n",
       "Population    1425.157323\n",
       "AveOccup         3.040518\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's first learn the mean from the train set\n",
    "\n",
    "means = X_train.mean(axis=0)\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc           14.500200\n",
       "HouseAge         51.000000\n",
       "AveRooms        131.687179\n",
       "AveBedrms        33.733333\n",
       "Population    35679.000000\n",
       "AveOccup        598.964286\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's now learn the min and max values, and the value range\n",
    "# from the train set\n",
    "\n",
    "ranges = X_train.max(axis=0) - X_train.min(axis=0)\n",
    "ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we are ready to perform mean normalization:\n",
    "\n",
    "X_train_scaled = (X_train - means) / ranges\n",
    "X_test_scaled = (X_test - means) / ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Mean Normalization with Scikit-learn: work-around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the StandardScaler so that it removes the mean\n",
    "# but does not divide by the standard deviation\n",
    "scaler_mean = StandardScaler(\n",
    "    with_mean=True, with_std=False).set_output(transform=\"pandas\")\n",
    "\n",
    "# set up the robustscaler so that it does NOT remove the median\n",
    "# but normalises by max()-min(), important for this to set up the\n",
    "# quantile range to 0 and 100, which represent the min and max values\n",
    "scaler_minmax = RobustScaler(\n",
    "    with_centering=False, with_scaling=True, quantile_range=(0, 100)\n",
    ").set_output(transform=\"pandas\")\n",
    "\n",
    "# fit the scalers to the train set, it will learn the parameters\n",
    "scaler_mean.fit(X_train)\n",
    "scaler_minmax.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler_minmax.transform(scaler_mean.transform(X_train))\n",
    "X_test_scaled = scaler_minmax.transform(scaler_mean.transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.880013</td>\n",
       "      <td>28.687984</td>\n",
       "      <td>5.442057</td>\n",
       "      <td>1.101109</td>\n",
       "      <td>1426.222061</td>\n",
       "      <td>3.140976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.920007</td>\n",
       "      <td>12.560416</td>\n",
       "      <td>2.862733</td>\n",
       "      <td>0.519956</td>\n",
       "      <td>1091.567168</td>\n",
       "      <td>15.796292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.465753</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.552150</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.414452</td>\n",
       "      <td>1.006494</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>2.436452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.529600</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.227365</td>\n",
       "      <td>1.048741</td>\n",
       "      <td>1169.500000</td>\n",
       "      <td>2.825041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.768750</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.064257</td>\n",
       "      <td>1.098434</td>\n",
       "      <td>1727.250000</td>\n",
       "      <td>3.285501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>25.636364</td>\n",
       "      <td>16305.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms    Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000   6192.000000   \n",
       "mean      3.880013    28.687984     5.442057     1.101109   1426.222061   \n",
       "std       1.920007    12.560416     2.862733     0.519956   1091.567168   \n",
       "min       0.499900     1.000000     1.465753     0.500000      8.000000   \n",
       "25%       2.552150    18.000000     4.414452     1.006494    796.000000   \n",
       "50%       3.529600    29.000000     5.227365     1.048741   1169.500000   \n",
       "75%       4.768750    37.000000     6.064257     1.098434   1727.250000   \n",
       "max      15.000100    52.000000   141.909091    25.636364  16305.000000   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      3.140976  \n",
       "std      15.796292  \n",
       "min       0.692308  \n",
       "25%       2.436452  \n",
       "50%       2.825041  \n",
       "75%       3.285501  \n",
       "max    1243.333333  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the original value statistics\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.132412</td>\n",
       "      <td>0.246283</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.015414</td>\n",
       "      <td>0.030594</td>\n",
       "      <td>0.026373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.232188</td>\n",
       "      <td>-0.541543</td>\n",
       "      <td>-0.030053</td>\n",
       "      <td>-0.017632</td>\n",
       "      <td>-0.039720</td>\n",
       "      <td>-0.003920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.090655</td>\n",
       "      <td>-0.208210</td>\n",
       "      <td>-0.007662</td>\n",
       "      <td>-0.002617</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.001009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.023246</td>\n",
       "      <td>0.007476</td>\n",
       "      <td>-0.001489</td>\n",
       "      <td>-0.001365</td>\n",
       "      <td>-0.007165</td>\n",
       "      <td>-0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.062212</td>\n",
       "      <td>0.164339</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.008467</td>\n",
       "      <td>0.000409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.767812</td>\n",
       "      <td>0.458457</td>\n",
       "      <td>1.036439</td>\n",
       "      <td>0.727517</td>\n",
       "      <td>0.417048</td>\n",
       "      <td>2.070729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms   Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000  6192.000000   \n",
       "mean      0.000920     0.001358     0.000142     0.000188     0.000030   \n",
       "std       0.132412     0.246283     0.021739     0.015414     0.030594   \n",
       "min      -0.232188    -0.541543    -0.030053    -0.017632    -0.039720   \n",
       "25%      -0.090655    -0.208210    -0.007662    -0.002617    -0.017634   \n",
       "50%      -0.023246     0.007476    -0.001489    -0.001365    -0.007165   \n",
       "75%       0.062212     0.164339     0.004866     0.000108     0.008467   \n",
       "max       0.767812     0.458457     1.036439     0.727517     0.417048   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      0.000168  \n",
       "std       0.026373  \n",
       "min      -0.003920  \n",
       "25%      -0.001009  \n",
       "50%      -0.000360  \n",
       "75%       0.000409  \n",
       "max       2.070729  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the values after scaling\n",
    "\n",
    "X_test_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L24Ap4GWt/LKqKuDaJEcmOaaq7hxKlBpLq7dctaD9dl10Vp8jkSRJGjwTRZKkYSvgI0kK+KOq2gpM9CR/7gIm2vKxwB09++5uZfslipJsorviiImJCaamppYu+iHZu3fv0Nq1ee2+Be0333iH1cbl1L6FxrpQy+V3OCjj3j5J0spkokiSNGw/VVV7kvwAsCPJ3/ZurKpqSaQ5a8mmrQDr1q2rycnJvgU7KqamphhWuzYu9Iqb8ybnVX9YbVxO7VtorAu1XH6HgzLu7ZMkrUwmivpkoZepS9JKV1V72s97krwfOBm4e3pIWZJjgHta9T3A8T27H9fKJEmSJPWBk1lLkoYmyaOTHDG9DJwOfBbYDmxo1TYAV7bl7cCL2t3PTgUecH4iSZIkqX+8okiSNEwTwPuTQNcnvauqPpTkeuCKJOcDXwTOafWvBs4EdgIPAi8efMiSJEnS+DJRJEkamqq6HXjaDOVfBk6bobyACwYQmiRJkrQiOfRMkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgTAqkNVSHIp8HPAPVX11Fb2eOA9wGpgF3BOVd2XJMAbgTOBB4GNVfWpts8G4JXtsK+pqm39bcrKsnrLVYess3ntPjYeUG/XRWctVUiSJEmSJGmZm8sVRW8HzjigbAtwTVWtAa5p6wDPBda0xybgbfBQYulC4BTgZODCJEctNnhJkiRJkiT1zyETRVX1CeDeA4rXA9NXBG0Dzu4pv6w61wJHJjkGeA6wo6rurar7gB18b/JJkiRJkiRJQ3TIoWezmKiqO9vyXcBEWz4WuKOn3u5WNlv590iyie5qJCYmJpiamlpgiIe2d+/evh1/89p9fTlOP00c/r1xLeXzOWj9/P2NqnFvo+2TJEmSpNGy0ETRQ6qqklQ/gmnH2wpsBVi3bl1NTk7269DfY2pqin4d/8C5gEbB5rX7uPim/X/Fu86bHE4wS6Cfv79RNe5ttH2SJEmSNFoWetezu9uQMtrPe1r5HuD4nnrHtbLZyiVJkiRJkjQiFpoo2g5saMsbgCt7yl+UzqnAA22I2oeB05Mc1SaxPr2VSZIkSZIkaUQccuhZkncDk8DRSXbT3b3sIuCKJOcDXwTOadWvBs4EdgIPAi8GqKp7k7wauL7Ve1VVHThBtiRJkiRJkobokImiqnrhLJtOm6FuARfMcpxLgUvnFZ0kSZIkSZIGZqFDzyRJkiRpoJLsSnJTkhuTfLKVPT7JjiS3tZ9HtfIkeVOSnUk+k+QZPcfZ0OrflmTDbOeTpJXIRJEkSZKk5eRnquqkqlrX1rcA11TVGuCatg7wXGBNe2wC3gZdYoluOo1TgJOBC6eTS5IkE0WSJEmSlrf1wLa2vA04u6f8supcCxzZ7tj8HGBHVd1bVfcBO4AzBhyzJI2sQ85RJEmSJEkjooCPJCngj6pqKzDR7rQMcBcw0ZaPBe7o2Xd3K5utfD9JNtFdicTExARTU1MLCnjz2n2zbps4fPbtCz3fsO3du3fZxj4b27Q82Kb+MVEkSZIkabn4qarak+QHgB1J/rZ3Y1VVSyItWktCbQVYt25dTU5OLug4G7dcNeu2zWv3cfFNM38k23Xews43bFNTUyz0uRpVtml5sE3949AzSZIkSctCVe1pP+8B3k83x9DdbUgZ7ec9rfoe4Pie3Y9rZbOVS5IwUSRJkiRpGUjy6CRHTC8DpwOfBbYD03cu2wBc2Za3Ay9qdz87FXigDVH7MHB6kqPaJNantzJJEg49kyRJkrQ8TADvTwLd55h3VdWHklwPXJHkfOCLwDmt/tXAmcBO4EHgxQBVdW+SVwPXt3qvqqp7B9cMSRptJookSZIkjbyquh142gzlXwZOm6G8gAtmOdalwKX9jlGSxoFDzyRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSs2rYAUiSJEnL2eotVy1ov10XndXnSCRJWjyvKJIkSZIkSRJgokiSJEmSJEmNQ88kSdJIc1iPJEnS4HhFkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSpBGQ5LAkn07ygbZ+QpLrkuxM8p4kD2/lj2jrO9v21UMNXJIkSRozq4YdgCRJwK8DtwKPbeuvA95QVZcn+UPgfOBt7ed9VfWkJOe2ei8YRsDSgVZvuWpe9Tev3cfGts+ui85aipAkSZLmzSuKJElDleQ44CzgT9p6gGcB721VtgFnt+X1bZ22/bRWX5IkSVIfeEWRJGnYfh/4LeCItv4E4P6q2tfWdwPHtuVjgTsAqmpfkgda/S/1HjDJJmATwMTEBFNTU0sY/nDs3bt3aO3avHbfoSvNYL7xTrdxUOebttDzzdfE4d8916jHOu3N77xyXvUnDu/2WXvs45YoouEa1t+oJElLyUSRJGlokvwccE9V3ZBksl/HraqtwFaAdevW1eRk3w49MqamphhWuzbOc4jVtF3nTc6r/nQbB3W+aQs933xtXruPi2/q3oqNeqwLNd3GhbZv1A3rb1SSpKVkokiSNEzPBJ6X5EzgkXRzFL0RODLJqnZV0XHAnlZ/D3A8sDvJKuBxwJcHH7YkSZI0npyjSJI0NFX18qo6rqpWA+cCH6uq84CPA89v1TYA0+Ndtrd12vaPVVUNMGRJkiRprJkokiSNopcBv5lkJ90cRJe08kuAJ7Ty3wS2DCk+SZIkaSw59EySNBKqagqYasu3AyfPUOfrwC8MNDBJkiRpBfGKIkmSJEmSJAEmiiRJkiRJktSYKJIkSZIkSRJgokiSJEmSJEnNohJFSf5jkpuTfDbJu5M8MskJSa5LsjPJe5I8vNV9RFvf2bav7ksLJEmSJEmS1BcLThQlORb4D8C6qnoqcBhwLvA64A1V9STgPuD8tsv5wH2t/A2tniRJkiRJkkbEYoeerQIOT7IKeBRwJ/As4L1t+zbg7La8vq3Ttp+WJIs8vyRJkiRJkvpkwYmiqtoD/B7w93QJogeAG4D7q2pfq7YbOLYtHwvc0fbd1+o/YaHnlyRJkiRJUn+tWuiOSY6iu0roBOB+4H8CZyw2oCSbgE0AExMTTE1NLfaQs9q7d2/fjr957b5DVxqwicO/N66lfD4HrZ+/v1E17m20fZIkSZI0WhacKAJ+FvhCVf0jQJL3Ac8Ejkyyql01dBywp9XfAxwP7G5D1R4HfPnAg1bVVmArwLp162pycnIRIR7c1NQU/Tr+xi1X9eU4/bR57T4uvmn/X/Gu8yaHE8wS6Ofvb1SNexttnyRJkiSNlsXMUfT3wKlJHtXmGjoNuAX4OPD8VmcDcGVb3t7Wads/VlW1iPNLkiRJkiSpjxYzR9F1dJNSfwq4qR1rK/Ay4DeT7KSbg+iStsslwBNa+W8CWxYRtyRJkiRJkvpsMUPPqKoLgQsPKL4dOHmGul8HfmEx55MkScvX6nkO0968dt9IDu2WNFxJDgM+Ceypqp9LcgJwOd2X1DcAv1RV30zyCOAy4Cfoprx4QVXtasd4OXA+8G3gP1TVhwffEkkaTYtKFGn5me+b9Gm7Ljqrz5FIkiRJC/LrwK3AY9v664A3VNXlSf6QLgH0tvbzvqp6UpJzW70XJDkROBd4CvBDwEeTPLmqvj3ohkjSKFrMHEWSJEmSNDBJjgPOAv6krQd4Ft2UGADbgLPb8vq2Ttt+Wqu/Hri8qr5RVV8AdjLDiAhJWqm8okiSJEnScvH7wG8BR7T1JwD3tzsuA+wGjm3LxwJ3AFTVviQPtPrHAtf2HLN3n4ck2QRsApiYmGBqampBAW9eu2/WbROHz759oecbtr179y7b2Gdjm5YH29Q/JookSZIkjbwkPwfcU1U3JJlc6vNV1Va6m/Wwbt26mpxc2CkPNtfa5rX7uPimmT+S7TpvYecbtqmpKRb6XI0q27Q82Kb+MVEkSZIkaTl4JvC8JGcCj6Sbo+iNwJFJVrWrio4D9rT6e4Djgd1JVgGPo5vUerp8Wu8+krTiOUeRJEmSpJFXVS+vquOqajXdZNQfq6rzgI8Dz2/VNgBXtuXtbZ22/WNVVa383CSPaHdMWwP89YCaIUkjzyuKJEmSJC1nLwMuT/Ia4NPAJa38EuAdSXYC99Ill6iqm5NcAdwC7AMu8I5nkvRdJookSZIkLStVNQVMteXbmeGuZVX1deAXZtn/tcBrly5CSVq+HHomSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKlZNewAJEkrV5JHAp8AHkHXJ723qi5McgJwOfAE4Abgl6rqm0keAVwG/ATwZeAFVbVrKMFLWnKrt1y1oP12XXRWnyORJGnl8IoiSdIwfQN4VlU9DTgJOCPJqcDrgDdU1ZOA+4DzW/3zgfta+RtaPUmSJEl9YqJIkjQ01dnbVh/WHgU8C3hvK98GnN2W17d12vbTkmQw0UqSJEnjz6FnkqShSnIY3fCyJwFvBT4P3F9V+1qV3cCxbflY4A6AqtqX5AG64WlfOuCYm4BNABMTE0xNTS1xKwZv7969Q2vX5rX7Dl2pDyYOX9y5Fvr8DKN9ox7rQk23cdDtG9T/xvT/4ajHKUnSfJgokiQNVVV9GzgpyZHA+4Ef7cMxtwJbAdatW1eTk5OLPeTImZqaYljt2rjAeWPma/PafVx808Lfquw6b3JB+w2jfaMe60JNt3HQ7Vvo+eZr+v9w1OOUJGk+HHomSRoJVXU/8HHgJ4Ejk0xnCI4D9rTlPcDxAG374+gmtZYkSZLUByaKJElDk+T725VEJDkceDZwK13C6Pmt2gbgyra8va3Ttn+sqmpgAUuSJEljzqFnkqRhOgbY1uYp+j7giqr6QJJbgMuTvAb4NHBJq38J8I4kO4F7gXOHEbQkSZI0rkwUSZKGpqo+Azx9hvLbgZNnKP868AsDCE2SJElakRx6JkmSJEmSJGCRiaIkRyZ5b5K/TXJrkp9M8vgkO5Lc1n4e1eomyZuS7EzymSTP6E8TJEmSJEmS1A+LvaLojcCHqupHgafRTUC6BbimqtYA17R1gOcCa9pjE/C2RZ5bkiRJkiRJfbTgOYqSPA74aWAjQFV9E/hmkvXAZKu2DZgCXgasBy5rd6e5tl2NdExV3bng6CVJksbA6i1XDTsESZIkYHGTWZ8A/CPwp0meBtwA/Dow0ZP8uQuYaMvHAnf07L+7le2XKEqyie6KIyYmJpiamlpEiAe3d+/e7zn+TXseWNCxNq/tQ0B9NnE4bF67ry/HWsrfw0LN9PsbN+PeRtsnSZIkSaNlMYmiVcAzgJdW1XVJ3sh3h5kBUFWVpOZz0KraCmwFWLduXU1OTi4ixIObmpriwONvHKNv9Dav3cfFN/Xnxna7zpvsy3H6aabf37gZ9zbaPkmSJEkaLYuZo2g3sLuqrmvr76VLHN2d5BiA9vOetn0PcHzP/se1MkmSJEk6qCSPTPLXSf4myc1J/msrPyHJde2mOe9J8vBW/oi2vrNtX91zrJe38s8lec6QmiRJI2nBiaKqugu4I8mPtKLTgFuA7cCGVrYBuLItbwde1O5+dirwgPMTSZIkSZqjbwDPqqqnAScBZ7TPFa8D3lBVTwLuA85v9c8H7mvlb2j1SHIicC7wFOAM4A+SHDbIhkjSKFvsXc9eCrwzyWfoXqx/F7gIeHaS24CfbesAVwO3AzuBPwZ+bZHnliRJkrRCVGdvW31YexTwLLrRDdDdTOfstry+rdO2n5YkrfzyqvpGVX2B7vPJyUvfAklaHhY1gU1V3Qism2HTaTPULeCCxZxPkiRJ0srVrvy5AXgS8Fbg88D9VTV9B5fpG+ZAz810qmpfkgeAJ7Tya3sO27tP77n6cpOdg91c5mA3n1muN8QYx5t52KblwTb1T39mOpYkSZKkJVZV3wZOSnIk8H7gR5fwXH25yc7BbpZzsJvPjOLNZOZiHG/mYZuWB9vUPyaKJEnSWFo9RncylbS/qro/yceBnwSOTLKqXVXUe8Oc6Zvp7E6yCngc8GW8yY4kHdRi5yiSJEmSpCWX5PvblUQkORx4NnAr8HHg+a3agTfTmb7JzvOBj7XpMLYD57a7op0ArAH+eiCNkKRlwCuKJEmSJC0HxwDb2jxF3wdcUVUfSHILcHmS1wCfBi5p9S8B3pFkJ3Av3Z3OqKqbk1xBd8fmfcAFbUibJAkTRZqjhV6+v+uis/ociSRJklaiqvoM8PQZym9nhruWVdXXgV+Y5VivBV7b7xglaRw49EySJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgTAqmEHIEmSFmf1lqsWtN+ui87qcySSJEla7ryiSJIkSZIkSYCJIkmSJEmSJDUmiiRJQ5Pk+CQfT3JLkpuT/Horf3ySHUluaz+PauVJ8qYkO5N8JskzhtsCSZIkabyYKJIkDdM+YHNVnQicClyQ5ERgC3BNVa0BrmnrAM8F1rTHJuBtgw9ZkiRJGl8miiRJQ1NVd1bVp9ryV4FbgWOB9cC2Vm0bcHZbXg9cVp1rgSOTHDPYqCVJkqTx5V3PJEkjIclq4OnAdcBEVd3ZNt0FTLTlY4E7enbb3cru7CkjySa6K46YmJhgampqyeIelr179z7Urs1r9y3oGAt9XhZ6vvmaOHxw5xqGcW8ffLeNg/5bG9T//PT/4ajHKUnSfJgokiQNXZLHAH8G/EZVfSXJQ9uqqpLUfI5XVVuBrQDr1q2rycnJPkY7Gqampphu18YtVy3oGLvOm1zQfgs933xtXruPi28a37cq494++G4bB/23ttDzzdf0/+GoxylJ0nw49EySNFRJHkaXJHpnVb2vFd89PaSs/bynle8Bju/Z/bhWJkmSJKkPFp0oSnJYkk8n+UBbPyHJde2ONO9J8vBW/oi2vrNtX73Yc0uSlrd0lw5dAtxaVa/v2bQd2NCWNwBX9pS/qN397FTggZ4hapIkSZIWqR9XFP063eSj014HvKGqngTcB5zfys8H7mvlb2j1JEkr2zOBXwKeleTG9jgTuAh4dpLbgJ9t6wBXA7cDO4E/Bn5tCDFLkiRJY2tRA+OTHAecBbwW+M32zfCzgH/TqmwDfofu9sXr2zLAe4G3JElVzWveCUnS+KiqvwQyy+bTZqhfwAVLGpQkSZK0gi12BsXfB34LOKKtPwG4v6qmb/0wfTca6LlTTVXtS/JAq/+l3gMO8k41vXeMmTZOdx8ZhbupDPr3N27GvY22T5IkSZJGy4ITRUl+Drinqm5IMtmvgAZ5p5reO8ZMG9SdXAZhFO6mspR385jp9zduxr2Ntk+SJM1VkuOBy4AJoICtVfXGJI8H3gOsBnYB51TVfW20wxuBM4EHgY1V9al2rA3AK9uhX1NV2wbZFkkaZYuZo+iZwPOS7AIupxty9kbgyCTT2Yneu9E8dKeatv1xwJcXcX5JkiRJK8c+YHNVnQicClyQ5ERgC3BNVa0BrmnrAM8F1rTHJrrpMGiJpQuBU4CTgQuTHDXIhkjSKFtwoqiqXl5Vx1XVauBc4GNVdR7wceD5rdqBd6qZvoPN81t95yeSJEmSdEhVdef0FUFV9VW6G+ocSzcX6vQVQduAs9vyeuCy6lxL94X2McBzgB1VdW9V3QfsAM4YXEskabQtxbiklwGXJ3kN8Gm62x7Tfr4jyU7gXrrkkiRJkiTNS5LVwNOB64CJqrqzbbqLbmga9MyR2kzPnzpb+YHn6MvcqQebM/Rgc4ou13kOx3GORtu0PNim/ulLoqiqpoCptnw73SWcB9b5OvAL/TifJEmSpJUpyWOAPwN+o6q+0k1F1KmqStKXUQv9mjv1YHOgHmxO0aWc63MpjeMcjbZpebBN/bOYOYokSZIkaWCSPIwuSfTOqnpfK767DSmj/bynlT80R2ozPX/qbOWSJJZm6JkkSZI0NKsHdBfbzWv3jdUdc0ddu4vZJcCtVfX6nk3Tc6FexPfOkfqSJJfTTVz9QFXdmeTDwO/2TGB9OvDyQbRBkpYDE0WSJEmSloNnAr8E3JTkxlb2CroE0RVJzge+CJzTtl0NnAnsBB4EXgxQVfcmeTVwfav3qqq6dyAtkKRlwESRJEkr1KCuupCkfqiqvwQyy+bTZqhfwAWzHOtS4NL+RSdJ48NEkSRJkpaUSUlJkpYPJ7OWJEmSJEkSYKJIkiRJkiRJjUPPtKQWeqn5rovO6nMkkiRJkiTpULyiSJIkSZIkSYCJIkmSJEmSJDUOPZMkqc8cditJkqTlyiuKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgTAqmEHIEmSOqu3XDXnupvX7mPjPOpLkiRJc+EVRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkoYsyaVJ7kny2Z6yxyfZkeS29vOoVp4kb0qyM8lnkjxjeJFLkiRJ48dEkSRp2N4OnHFA2RbgmqpaA1zT1gGeC6xpj03A2wYUoyRJkrQimCiSJA1VVX0CuPeA4vXAtra8DTi7p/yy6lwLHJnkmIEEKkmSJK0Aqxa6Y5LjgcuACaCArVX1xiSPB94DrAZ2AedU1X1JArwROBN4ENhYVZ9aXPiSpDE1UVV3tuW76PoagGOBO3rq7W5ld/aUkWQT3RVHTExMMDU1taTBHmjz2n1Lfo6JwwdznmEa9zaOe/tg/Nu42PYN+rVJkqS5WHCiCNgHbK6qTyU5ArghyQ5gI91wgYuSbKEbLvAy9h8ucArdcIFTFhO8JGn8VVUlqXnusxXYCrBu3bqanJxcitBmtXHLVUt+js1r93HxTYvpxkffuLdx3NsH49/GxbZv13mT/QtGkqQ+WfDQs6q6c/qKoKr6KnAr3be6DheQJC3W3dN9RPt5TyvfAxzfU++4ViZJGnP9uvlBkg2t/m1JNgyjLZI0yvoyR1GS1cDTgeuY/3ABSZIOtB2YfvO+Abiyp/xF7QPAqcADPX2OJGm8vZ1F3vygTZNxId3IhpOBC6eTS5KkzqKvBU7yGODPgN+oqq90UxF1FjJcYJDzSuzdu/d7jj9O4+iX87wAb37nlYesM3H499Zbe+zjliqkoZjpb3Sc2D4BJHk3MAkcnWQ33Rv4i4ArkpwPfBE4p1W/mm6uu5108929eOABS5KGoqo+0b6g7rWerg+BbjTDFN20Fw+NZgCuTTI9mmES2FFV9wK0qTPOAN691PFL0nKxqERRkofRJYneWVXva8V3Jzmmqu5cyHCBQc4rMTU1xYHHH8S8EoOyEucFGLex/jP9jY4T2yeAqnrhLJtOm6FuARcsbUSSpGVkvqMZHOUgSYewmLueBbgEuLWqXt+zaXq4wEV873CBlyS5nO5ST4cLSJIkSeqLhYxmOJh+jXQ42BX+BxsBsFyvSh7HK6pt0/Jgm/pnMZebPBP4JeCmJDe2slfgcAFJkiRJgzHf0Qx7+O5QtenyqZkO3K+RDgcbsXCwEQDL9Ur5cbyi2jYtD7apfxacKKqqvwQyy2aHC0iSJElaavMazZDkw8Dv9kxgfTrw8gHHLEkjbXwnsJEkSZI0Nvpx84OqujfJq4HrW71XTU9sLUnqmCiSJEmSNPL6dfODqroUuLSPoUnSWPm+YQcgSZIkSZKk0WCiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNauGHYDUT6u3XLWg/XZddFafI5EkSZIkafnxiiJJkiRJkiQBJookSZIkSZLUmCiSJEmSJEkSYKJIkiRJkiRJjZNZSyx8EmxwImxJkiRJ0vgY20TRXD74b167j42LSBBIkiRJkiSNE4eeSZIkSZIkCRjjK4okSVqsxQxLlSRJkpYjryiSJEmSJEkSYKJIkiRJkiRJjYkiSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEmCiSJIkSZIkSY2JIkmSJEmSJAEmiiRJkiRJktSYKJIkSZIkSRJgokiSJEmSJEmNiSJJkiRJkiQBsGrYAUjL3eotVy1ov10XnbUszidJkiRJWjm8okiSJEmSJEmAVxRJQzOXK4M2r93HxgVeQSRJkiRJ0nyZKJK0ojmUT5IkSZK+y0SRtEKYEJEkSVo+fO8maVgGPkdRkjOSfC7JziRbBn1+SdLyZ18iSVos+xJJmtlAE0VJDgPeCjwXOBF4YZITBxmDJGl5sy+RJC2WfYkkzW7QQ89OBnZW1e0ASS4H1gO3DDgOSXO00Mue56Kfk3V7mfWKYl8iSVqsse1LHLImabFSVYM7WfJ84Iyq+pW2/kvAKVX1kp46m4BNbfVHgM8tYUhHA19awuMPm+1b/sa9jSuxfU+squ8fRjDjYgT7kmEZ9/8fGP82jnv7YPzbOKz22Zcs0gj1JeP4P2KblgfbtDwsZZtm7UtGbjLrqtoKbB3EuZJ8sqrWDeJcw2D7lr9xb6Pt01IZZF8yLCvh72vc2zju7YPxb+O4t2+lG0RfMo5/Q7ZpebBNy8Ow2jToyaz3AMf3rB/XyiRJmiv7EknSYtmXSNIsBp0ouh5Yk+SEJA8HzgW2DzgGSdLyZl8iSVos+xJJmsVAh55V1b4kLwE+DBwGXFpVNw8yhgOM9bAEbN84GPc22j7N2wj2JcOyEv6+xr2N494+GP82jnv7xtYI9SXj+Ddkm5YH27Q8DKVNA53MWpIkSZIkSaNr0EPPJEmSJEmSNKJMFEmSJEmSJAlYYYmiJI9PsiPJbe3nUTPUOSnJXyW5OclnkrxgGLHOR5Izknwuyc4kW2bY/ogk72nbr0uyeghhLtgc2vebSW5pv69rkjxxGHEu1KHa11PvXyepJMvulo9zaWOSc9rv8eYk7xp0jIsxh7/RH07y8SSfbn+nZw4jTi1vc+nDeuo+NsnuJG8ZZIyLZT9tPz2qVkJfraU1jq8D4/i/P47/6+P4Pnzc3nsnuTTJPUk+O8v2JHlTa+9nkjxjyYOqqhXzAP4bsKUtbwFeN0OdJwNr2vIPAXcCRw479oO06TDg88A/Ax4O/A1w4gF1fg34w7Z8LvCeYcfd5/b9DPCotvzvx619rd4RwCeAa4F1w457CX6Ha4BPA0e19R8Ydtx9bt9W4N+35ROBXcOO28fye8ylD+up+0bgXcBbhh13v9toPz1aj3Hvp+faxlZv2fbVPpb2MY6vA+P4vz+O/+vj+D58HN97Az8NPAP47CzbzwQ+CAQ4FbhuqWNaUVcUAeuBbW15G3D2gRWq6u+q6ra2/A/APcD3DyrABTgZ2FlVt1fVN4HL6drZq7fd7wVOS5IBxrgYh2xfVX28qh5sq9cCxw04xsWYy+8P4NXA64CvDzK4PplLG/8t8Naqug+gqu4ZcIyLMZf2FfDYtvw44B8GGJ/GxyH7MIAkPwFMAB8ZTFh9ZT9tPz2KVkJfraU1jq8D4/i/P47/6+P4Pnzs3ntX1SeAew9SZT1wWXWuBY5McsxSxrTSEkUTVXVnW76L7o30rJKcTJel/PxSB7YIxwJ39KzvbmUz1qmqfcADwBMGEt3izaV9vc6ny7YuF4dsX7u08PiqumqQgfXRXH6HTwaenOT/JLk2yRkDi27x5tK+3wF+Mclu4GrgpYMJTWPmkH1Yku8DLgb+0yAD6yP7afvpUbQS+motrXF8HRjH//1x/F8fx/fhK/G993z/3xZt1VIefBiSfBT4wRk2/XbvSlVVkjrIcY4B3gFsqKrv9DdKLYUkvwisA/7FsGPpl/ah7/XAxiGHstRW0V32Okn3bdMnkqytqvuHGVQfvRB4e1VdnOQngXckeaqvLTpQH/qwXwOurqrdo/pFtP30yjWO/TSsqL5aWpBx+d8f4//1cXwf7nvvRRq7RFFV/exs25LcneSYqrqzvcGc8bK6JI8FrgJ+u13aNcr2AMf3rB/XymaqszvJKrrL7748mPAWbS7tI8nP0n3I+BdV9Y0BxdYPh2rfEcBTgan2oe8Hge1JnldVnxxYlIszl9/hbrqxtt8CvpDk7+g6rOsHE+KizKV95wNnAFTVXyV5JHA0s7wGaeXqQx/2k8D/l+TXgMcAD0+yt6pmnZBz0Oyn7aeXWT8NK6Ov1tIax9eBcfzfH8f/9XF8H74S33vP6f+tn1ba0LPtwIa2vAG48sAKSR4OvJ9uDOB7BxjbQl0PrElyQov9XLp29upt9/OBj1WbFWsZOGT7kjwd+CPgectgTO2BDtq+qnqgqo6uqtVVtZpufPcod0Yzmcvf6J/TfYtBkqPpLoG9fYAxLsZc2vf3wGkASX4MeCTwjwONUuPgkH1YVZ1XVT/cXi/+E11fNjJJojmwn7afHkUroa/W0hrH14Fx/N8fx//1cXwfvhLfe28HXtTufnYq8EDPUP2lUSMwy/egHnTjfK8BbgM+Cjy+la8D/qQt/yLwLeDGnsdJw479EO06E/g7ujkafruVvYruhQu6f4z/CewE/hr4Z8OOuc/t+yhwd8/va/uwY+5n+w6oO8WI311hgb/D0F3KewtwE3DusGPuc/tOBP4P3V0ZbgROH3bMPpbfYy592AH1N7L87npmP20/PZKPldBX+1jaxzi+Dozj//44/q/P4fe07N6Hz6FNy+q9N/Buuru4fovuCq/zgV8FfrXnd/TW1t6bBvF3l3ZiSZIkSZIkrXArbeiZJEmSJEmSZmGiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBJgokiRJkiRJUmOiSJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiagySV5EnDjkOSNHqSvD3Jaxax/94k/6yfMUmSJC2UiSKNjCRTSe5L8og+HGtXkn9qb77vS3JVkuP7EackabQd0Afc3RI5jxl2XPBQX/crvWVV9Ziqun1YMUmSDq2fn1Xa8TYmuSnJg0nuSvK2JEf249jSYpko0khIshr4/4ACntenw/58VT0GOAa4G3hzn467nySHLcVxJUmLMt0HPANYB7xyyPFIkpapfn9WSbIZeB3wn4HHAacCTwR2JHn4Yo8vLZaJIo2KFwHXAm8HNiR5RJL7kzx1ukKS72/fEP9AW/+5JDe2ev83yY/PdOCq+jrwXuDEnmM9IsnvJfn79m3zHyY5vGf7f05yZ5J/SPLLvcdr30y/LcnVSb4G/Ez79vo/J/lMkq8luSTJRJIPJvlqko8mOart/8gk/yPJl1vs1yeZ6NszKUl6SFXtAT4IPDXJ85Lc3F57p5L82HS99jr+8iS3tG+M/zTJI9u2jUn+sve4sw1JTnJUkg8k+cd2nA8kOa5tey3dB423tKud3nLgsZI8Lsllbf8vJnllku/rjaP1X/cl+UKS5y7NMydJ6tG3zypJHgv8V+ClVfWhqvpWVe0CzgFWA7/Y6h2W5BVJPt8+T9wwPUIiyVOS7Ehyb/ss84pWvt9Q6CSTSXb3rM/a10m9TBRpVLwIeGd7PAc4Engf8MKeOucAf1FV9yR5OnAp8O+AJwB/BGzPDJeCJnkU8AK6F/dpFwFPBk4CngQcC/yXVv8M4D8BzwbWAD87Q7z/BngtcAQw/eHhX7d9ngz8PN0Hk1cA30/3v/YfWr0NdN8cHN9i/1Xgnw725EiSFqa9qT4T+CrwbuA36F6Xrwb+1wHf3J5H1wf9c7rX8oVchfR9wJ/SfTP8w3Sv728BqKrfBv438JI23OwlM+z/Zro+4p8B/4Kuf3xxz/ZTgM8BRwP/DbgkSRYQpyRp7vr5WeX/BR7Z9n9IVe2l65ue3Yp+sx3/TOCxwC8DDyY5Avgo8CHgh+g+y1wzj7b0o6/TmDNRpKFL8lN0b6ivqKobgM/TJWLeBZzbU3W6DGAT8EdVdV1VfbuqtgHfoLtsc9qfJ7kfeIDuBfe/t/Ol7f8fq+reqvoq8Ls95zoH+NOq+mxVfQ34nRnCvrKq/k9VfaddsQTw5qq6u317/b+B66rq0237+4Gnt3rfouswntRiv6GqvjK/Z02SdAjTfcBfAn8B3AJcVVU7qupbwO8Bh9O9YZ/2lqq6o6rupfsy4IXMU1V9uar+rKoebP3La+kSPoeUbijzucDLq+qr7Rvmi4Ff6qn2xar646r6NrCNbni1V6VK0hJZgs8qRwNfqqp9M5zuzrYd4FeAV1bV56rzN1X1ZeDngLuq6uKq+nrrL66bR5MW3ddp/K0adgAS3RU2H6mqL7X1d7WynwAeleQUujmGTqJLuED3Yr0hyUt7jvNwuqz6tLOr6qPtjfd64C+SnAh8B3gUcEPPl7ABpuca+iHghp7jfHGGmO+YoezunuV/mmF9eiLVd9BdTXR5ugnr/gfw2+2DiySpP86uqo9OryR5Gz2v51X1nSR30F1ROq33tf2L7N+nzEm7ivUNwBnAUa34iCSHteTOwRwNPIz9+50vHhDjXdMLVfVg68dGYqJuSRpT/f6s8iXg6CSrZkgWHdO2Q/d54fMzxDNb+Vwtuq/T+DNRpKFKNy/QOcBhSabf/D6C7nLOpwJX0GW57wY+0L6dhe4F7rVV9dpDnaO9MX9fkj8CforuMs9/Ap7Srv450J10L8DTfnimwx7qvAeJ51t045L/a7qJ8a6mG0ZwyUKPKUk6pH8A1k6vtKtLjwd6+4EDX/v/oS1/je4Lhul9f/Ag59kM/AhwSlXdleQk4NN0X0jAwfuPL9FddfpEuiugpuOYqa+SJC2xpfiskuRxdFcX/au2/3T5Y4Dn0k1dMX2Mfw589oBD3MH+VzL12q+/Ambqr2br66SHOPRMw3Y28G26iaZPao8foxu69SK6jP0L6MbSvqtnvz8GfjXJKek8OslZbczuftr29XTf7N5aVd9p+7+hZ7K5Y5M8p+1yBbAxyYntm+EL+9ngJD+TZG270ukrdB8KvtPPc0iSvscVwFlJTkvyMLqEzjeA/9tT54IkxyV5PPDbwHta+d8AT0lyUpv083cOcp4j6L6MuL8d58A+5G66+Ye+R/ti4wrgtUmOSPJEujkq/sc82ilJ6p+z6fNnlap6gO5L4zcnOSPJw9qXx1cAu+lGHwD8CfDqJGvaMX48yROADwDHJPmNdJNqH9GuagK4ETgzyePblxq/MUObZuvrpIeYKNKwbaCbD+jvq+qu6QfdxJ/n0Q0B+xrdJZEfnN6pqj4J/NtW7z5gJ7DxgGP/ryR76ZIxrwU2VNXNbdvL2j7XJvkK3YRwP9KO/UHg94GPtTof63Obf5DuLmxfAW6lmzvjHQfdQ5K0KFX1Obo7ybyZ7sqdnwd+vqq+2VPtXcBHgNvpLut/Tdv374BX0fUVt/HdmxjM5Pfp5j76Et1NFD50wPY3As9vd5t50wz7v5Su37u9nedddBOiSpIGb0k+q1TVf6O7cuj36D4TXEd3pdBpVfWNVu31dMmjj7Q6lwCHt6uWnk3Xj91F1y/9TNvnHXRfbuxq+82UBJqxr5N6pWrBI2gkSZLGQpJdwK/0zmskSdI4sa/TXHlFkSRJkiRJkgATRZIkSZIkSWoceiZJkiRJkiTAK4okSZIkSZLUrBp2AAdz9NFH1+rVq/t6zK997Ws8+tGP7usx+8G45mcU4xrFmMC45mup47rhhhu+VFXfv2Qn0PdYir6kX0b1/2CprcR2r8Q2g+1eKvYlg7eYvmSl/h/Mxudjfz4f+/P52N9SPh8H60tGOlG0evVqPvnJT/b1mFNTU0xOTvb1mP1gXPMzinGNYkxgXPO11HEl+eKSHVwzWoq+pF9G9f9gqa3Edq/ENoPtXir2JYO3mL5kpf4fzMbnY38+H/vz+djfUj4fB+tLHHomSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpWTXsAJbK6i1XzVi+ee0+Ns6yDWDXRWctVUiSpBVitj7oQAf2SfZBkjR+btrzwEE/f8zGPkHSsMzpiqIkRyZ5b5K/TXJrkp9M8vgkO5Lc1n4e1eomyZuS7EzymSTP6DnOhlb/tiQblqpRkiRJkiRJmr+5Dj17I/ChqvpR4GnArcAW4JqqWgNc09YBngusaY9NwNsAkjweuBA4BTgZuHA6uSRJkiRJkqThO2SiKMnjgJ8GLgGoqm9W1f3AemBbq7YNOLstrwcuq861wJFJjgGeA+yoqnur6j5gB3BGH9siSZIkSZKkRZjLHEUnAP8I/GmSpwE3AL8OTFTVna3OXcBEWz4WuKNn/92tbLby/STZRHclEhMTE0xNTc21LfvZvHbfjOUTh8++DVjw+RZr7969Qzv3wRjX3I1iTGBc8zWqcUmSJEnSIMwlUbQKeAbw0qq6Lskb+e4wMwCqqpJUPwKqqq3AVoB169bV5OTkgo4z24Rxm9fu4+KbZm/2rvMWdr7FmpqaYqFtXUrGNXejGBMY13yNalySJKmbOxX4E+CpQAG/DHwOeA+wGtgFnFNV9yUJ3RQaZwIPAhur6lPtOBuAV7bDvqaqtiFJAuY2R9FuYHdVXdfW30uXOLq7DSmj/bynbd8DHN+z/3GtbLZySZIkSZoL506VpCV2yERRVd0F3JHkR1rRacAtwHZg+s5lG4Ar2/J24EXt7menAg+0IWofBk5PclR7IT69lUmSJEnSQTl3qiQNxlyGngG8FHhnkocDtwMvpksyXZHkfOCLwDmt7tV0l3fupLvE88UAVXVvklcD17d6r6qqe/vSCkmSJEnjblnOnXqoOVJnM65zJjof5P58Pvbn87G/YT0fc0oUVdWNwLoZNp02Q90CLpjlOJcCl84jPkmSJEmCZTp36pvfeeVB50idzbDmTl1qzge5P5+P/fl87G9Yz8dc5iiSJEmSpGFz7lRJGgATRZIkSZJGnnOnStJgzP8aSEmSJEkaDudOlaQlZqJIkiRJ0rLg3KmStPQceiZJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSBiDJ8Uk+nuSWJDcn+fVW/jtJ9iS5sT3O7Nnn5Ul2Jvlckuf0lJ/RynYm2TKM9kiSJEnjatWwA5AkrQj7gM1V9akkRwA3JNnRtr2hqn6vt3KSE4FzgacAPwR8NMmT2+a3As8GdgPXJ9leVbcMpBWSJEnSmDNRJElaclV1J3BnW/5qkluBYw+yy3rg8qr6BvCFJDuBk9u2nVV1O0CSy1tdE0WSJElSH5gokiQNVJLVwNOB64BnAi9J8iLgk3RXHd1Hl0S6tme33Xw3sXTHAeWnzHCOTcAmgImJCaampvrbiEPYvHbfnOpNHL5/3UHHOSx79+5dMW2dthLbDLZbkqTlyESRJGlgkjwG+DPgN6rqK0neBrwaqPbzYuCXF3ueqtoKbAVYt25dTU5OLvaQ87Jxy1Vzqrd57T4uvum7XfGu8yaXKKLRMjU1xaB/J8O2EtsMtluSpOXIRJEkaSCSPIwuSfTOqnofQFXd3bP9j4EPtNU9wPE9ux/XyjhIuSRJkqRF8q5nkqQllyTAJcCtVfX6nvJjeqr9S+CzbXk7cG6SRyQ5AVgD/DVwPbAmyQlJHk434fX2QbRBkiRJWgm8okiSNAjPBH4JuCnJja3sFcALk5xEN/RsF/DvAKrq5iRX0E1SvQ+4oKq+DZDkJcCHgcOAS6vq5sE1Q5IkSRpvJookSUuuqv4SyAybrj7IPq8FXjtD+dUH20+SJEnSwjn0TJIkSZIkSYCJIkmSJEmSJDUmiiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1JookSZIkSZIEmCiSJEmSJElSY6JIkiRJkiRJgIkiSZIkSZIkNSaKJEmSJEmSBMwxUZRkV5KbktyY5JOt7PFJdiS5rf08qpUnyZuS7EzymSTP6DnOhlb/tiQblqZJkiRJkiRJWoj5XFH0M1V1UlWta+tbgGuqag1wTVsHeC6wpj02AW+DLrEEXAicApwMXDidXJIkSZIkSdLwLWbo2XpgW1veBpzdU35Zda4FjkxyDPAcYEdV3VtV9wE7gDMWcX5JkiRJkiT10ao51ivgI0kK+KOq2gpMVNWdbftdwERbPha4o2ff3a1stvL9JNlEdyUSExMTTE1NzTHE/W1eu2/G8onDZ98GLPh8i7V3796hnftgjGvuRjEmMK75GtW4JElSNyUG8FXg28C+qlrXRi68B1gN7ALOqar7kgR4I3Am8CCwsao+1Y6zAXhlO+xrqmobkiRg7omin6qqPUl+ANiR5G97N1ZVtSTSorUk1FaAdevW1eTk5IKOs3HLVTOWb167j4tvmr3Zu85b2PkWa2pqioW2dSkZ19yNYkxgXPM1qnFJkqSH/ExVfalnfXpKjIuSbGnrL2P/KTFOoZsS45SeKTHW0X0hfkOS7W3UgySteHMaelZVe9rPe4D3080xdHcbUkb7eU+rvgc4vmf341rZbOWSJEmStFBOiSFJfXTIRFGSRyc5YnoZOB34LLAdmL5z2Qbgyra8HXhRu/vZqcADbYjah4HTkxzVJrE+vZVJkiRJ0lxMT4lxQ5uyApZoSgxJWqnmMvRsAnh/N8SXVcC7qupDSa4HrkhyPvBF4JxW/2q6ccA76cYCvxigqu5N8mrg+lbvVVV1b99aIkmSJGncDWxKjH7NnXqoOVJnM65zJjof5P58Pvbn87G/YT0fh0wUVdXtwNNmKP8ycNoM5QVcMMuxLgUunX+YkiRJkla63ikxkuw3JUZV3TmPKTEmDyifmuFcfZk79c3vvPKgc6TOZlhzpy4154Pcn8/H/nw+9jes52NOcxRJkiRJ0jA5JYYkDcb8U9uSJEmSNHhOiSFJA2CiSJIkSdLIc0oMSRoMh55JkiRJkiQJMFEkSZIkSZKkxkSRJEmSJEmSABNFkiRJkiRJakwUSZIkSZIkCTBRJEmSJEmSpMZEkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSJEmSJElqTBRJkiRJkiQJMFEkSZIkSZKkxkSRJEmSJEmSABNFkiRJkiRJakwUSZKWXJLjk3w8yS1Jbk7y66388Ul2JLmt/TyqlSfJm5LsTPKZJM/oOdaGVv+2JBuG1SZJkiRpHJkokiQNwj5gc1WdCJwKXJDkRGALcE1VrQGuaesAzwXWtMcm4G3QJZaAC4FTgJOBC6eTS5IkSZIWz0SRJGnJVdWdVfWptvxV4FbgWGA9sK1V2wac3ZbXA5dV51rgyCTHAM8BdlTVvVV1H7ADOGNwLZEkSZLG26phByBJWlmSrAaeDlwHTFTVnW3TXcBEWz4WuKNnt92tbLbyA8+xie5KJCYmJpiamupfA+Zg89p9c6o3cfj+dQcd57Ds3bt3xbR12kpsM9huSZKWIxNFkqSBSfIY4M+A36iqryR5aFtVVZLqx3mqaiuwFWDdunU1OTnZj8PO2cYtV82p3ua1+7j4pu92xbvOm1yiiEbL1NQUg/6dDNtKbDPYbkmSliOHnkmSBiLJw+iSRO+sqve14rvbkDLaz3ta+R7g+J7dj2tls5VLkiRJ6gMTRZKkJZfu0qFLgFur6vU9m7YD03cu2wBc2VP+onb3s1OBB9oQtQ8Dpyc5qk1ifXorkyRJktQHDj2TJA3CM4FfAm5KcmMrewVwEXBFkvOBLwLntG1XA2cCO4EHgRcDVNW9SV4NXN/qvaqq7h1ICyRJkqQVwESRJGnJVdVfApll82kz1C/gglmOdSlwaf+ikyRJkjTNoWeSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJGAeiaIkhyX5dJIPtPUTklyXZGeS9yR5eCt/RFvf2bav7jnGy1v555I8p++tkSRJkiRJ0oLN54qiXwdu7Vl/HfCGqnoScB9wfis/H7ivlb+h1SPJicC5wFOAM4A/SHLY4sKXJEmSJElSv8wpUZTkOOAs4E/aeoBnAe9tVbYBZ7fl9W2dtv20Vn89cHlVfaOqvgDsBE7uQxskSZIkSZLUB3O9ouj3gd8CvtPWnwDcX1X72vpu4Ni2fCxwB0Db/kCr/1D5DPtIkiRJ0iE5JYYkLa1Vh6qQ5OeAe6rqhiSTSx1Qkk3AJoCJiQmmpqYWdJzNa/fNWD5x+OzbgAWfb7H27t07tHMfjHHN3SjGBMY1X6MalyRJesj0lBiPbevTU2JcnuQP6abCeBs9U2IkObfVe8EBU2L8EPDRJE+uqm8PuiGSNIoOmSgCngk8L8mZwCPpXpDfCByZZFW7aug4YE+rvwc4HtidZBXwOODLPeXTevd5SFVtBbYCrFu3riYnJxfQLNi45aoZyzev3cfFN83e7F3nLex8izU1NcVC27qUjGvuRjEmMK75GtW4JEnSflNivBb4zZ4pMf5Nq7IN+B26RNH6tgzdlBhvOXBKDOALSaanxPirATVDkkbaIRNFVfVy4OUA7Yqi/1RV5yX5n8DzgcuBDcCVbZftbf2v2vaPVVUl2Q68K8nr6TL3a4C/7mtrJEmSJI2z36ebEuOItj7nKTGS9E6JcW3PMWecEqNfIx0ONaJhNuN6hbNXb+/P52N/Ph/7G9bzMZcrimbzMuDyJK8BPg1c0sovAd7RMvP30l3WSVXdnOQK4BZgH3CBl3dKkiRJmotBT4nRr5EOb37nlQcd0TCbYY10WGpevb0/n4/9+Xzsb1jPx7xesapqCphqy7czw13LqurrwC/Msv9r6S4TlSRJkqT5GOiUGJK0Us31rmeSJEmSNDRV9fKqOq6qVtONWvhYVZ0HfJxuyguYeUoM6JkSo5Wf2+6KdgJOiSFJ+1nM0DNJkiRJGjanxJCkPjJRJEmSJGlZcUoMSVo6Dj2TJEmSJEkSYKJIkiRJkiRJjYkiSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEmCiSJIkSZIkSY2JIkmSJEmSJAEmiiRJkiRJktSsGnYAkiSNqtVbrhp2CJIkSdJAeUWRJEmSJEmSABNFkiRJkiRJakwUSZIkSZIkCTBRJEmSJEmSpMZEkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSJEmSJElqTBRJkiRJkiQJMFEkSZIkSZKkxkSRJGnJJbk0yT1JPttT9jtJ9iS5sT3O7Nn28iQ7k3wuyXN6ys9oZTuTbBl0OyRJkqRxZ6JIkjQIbwfOmKH8DVV1UntcDZDkROBc4Cltnz9IcliSw4C3As8FTgRe2OpKkiRJ6pNVww5AkjT+quoTSVbPsfp64PKq+gbwhSQ7gZPbtp1VdTtAkstb3Vv6Ha8kSZK0UpkokiQN00uSvAj4JLC5qu4DjgWu7amzu5UB3HFA+SkzHTTJJmATwMTEBFNTUwsKbvPafQvab64mDt//HAuNc7nZu3fvimnrtJXYZrDdkiQtRyaKJEnD8jbg1UC1nxcDv9yPA1fVVmArwLp162pycnJBx9m45ap+hDOrzWv3cfFN3+2Kd503uaTnGxVTU1Ms9HeyXK3ENoPtliRpOTJRJEkaiqq6e3o5yR8DH2ire4Dje6oe18o4SLkkSZKkPnAya0nSUCQ5pmf1XwLTd0TbDpyb5BFJTgDWAH8NXA+sSXJCkofTTXi9fZAxS5IkSePOK4okSUsuybuBSeDoJLuBC4HJJCfRDT3bBfw7gKq6OckVdJNU7wMuqKpvt+O8BPgwcBhwaVXdPNiWSJIkSePNRJEkaclV1QtnKL7kIPVfC7x2hvKrgav7GJokSZKkHg49kyRJkiRJEmCiSJIkSZIkSY2JIkmSJEmSJAFzSBQleWSSv07yN0luTvJfW/kJSa5LsjPJe9odaGh3qXlPK78uyeqeY728lX8uyXOWrFWSJEmSJEmat7lcUfQN4FlV9TTgJOCMJKcCrwPeUFVPAu4Dzm/1zwfua+VvaPVIciLdrYyfApwB/EGSw/rYFkmSJEmSJC3CIRNF1dnbVh/WHgU8C3hvK98GnN2W17d12vbTkqSVX15V36iqLwA7gZP70QhJkiRJ482RDpI0GKvmUqld+XMD8CTgrcDngfural+rshs4ti0fC9wBUFX7kjwAPKGVX9tz2N59es+1CdgEMDExwdTU1Pxa1Gxeu2/G8onDZ98GLPh8i7V3796hnftgjGvuRjEmMK75GtW4JEnSQyMd9iZ5GPCXST4I/CbdSIfLk/wh3QiHt9Ez0iHJuXQjHV5wwEiHHwI+muTJVfXtYTRKkkbNnBJF7UXzpCRHAu8HfnSpAqqqrcBWgHXr1tXk5OSCjrNxy1Uzlm9eu4+Lb5q92bvOW9j5FmtqaoqFtnUpGdfcjWJMYFzzNapxSZK00lVVAbONdPg3rXwb8Dt0iaL1bRm6kQ5vOXCkA/CFJNMjHf5q6VshSaNvTomiaVV1f5KPAz8JHJlkVbuq6DhgT6u2Bzge2J1kFfA44Ms95dN695EkSZKkg1qOIx0ONaJhNuN6hbNXb+/P52N/Ph/7G9bzcchEUZLvB77VkkSHA8+mu2zz48DzgcuBDcCVbZftbf2v2vaPVVUl2Q68K8nr6S7xXAP8dZ/bI0mSJGlMLceRDm9+55UHHdEwm2GNdFhqXr29P5+P/fl87G9Yz8dcXrGOAba17P33AVdU1QeS3AJcnuQ1wKeBS1r9S4B3tEs476Ub/0tV3ZzkCuAWYB9wgeOAJUmSJM2XIx0kaekcMlFUVZ8Bnj5D+e3McNeyqvo68AuzHOu1wGvnH6YkSZKklcyRDpI0GPO/BlKSJEmSBs+RDpI0ACaKJEmSJI08RzpI0mB837ADkCRJkiRJ0mgwUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJWnJJLk1yT5LP9pQ9PsmOJLe1n0e18iR5U5KdST6T5Bk9+2xo9W9LsmEYbZEkSZLG2aphByBJWhHeDrwFuKynbAtwTVVdlGRLW38Z8FxgTXucArwNOCXJ44ELgXVAATck2V5V9w2sFUts9ZarFrTfrovO6nMkkiRJWqm8okiStOSq6hPAvQcUrwe2teVtwNk95ZdV51rgyCTHAM8BdlTVvS05tAM4Y8mDlyRJklYQryiSJA3LRFXd2ZbvAiba8rHAHT31drey2cq/R5JNwCaAiYkJpqamFhTg5rX7FrTfXE0c3p9zLLR9w7J3795lF/NircQ2g+2WJGk5MlEkSRq6qqok1cfjbQW2Aqxbt64mJycXdJyNCxwKNleb1+7j4psW3xXvOm9y8cEM0NTUFAv9nSxXK7HNYLslSVqOHHomSRqWu9uQMtrPe1r5HuD4nnrHtbLZyiVJkiT1iYkiSdKwbAem71y2Abiyp/xF7e5npwIPtCFqHwZOT3JUu0Pa6a1MkiRJUp849EyStOSSvBuYBI5Ospvu7mUXAVckOR/4InBOq341cCawE3gQeDFAVd2b5NXA9a3eq6rqwAmyJUmSJC3CIRNFSY6nu53xBN3tiLdW1RvbbYrfA6wGdgHnVNV9SQK8ke5N/oPAxqr6VDvWBuCV7dCvqaptSJLGXlW9cJZNp81Qt4ALZjnOpcClfQxNkiRJUo+5DD3bB2yuqhOBU4ELkpwIbAGuqao1wDVtHeC5wJr22AS8DaAlli4ETgFOBi5sQwckSZIk6aCSHJ/k40luSXJzkl9v5Y9PsiPJbe3nUa08Sd6UZGeSzyR5Rs+xNrT6t7UvsyVJzSETRVV15/QVQVX1VeBWutsRrwemrwjaBpzdltcDl1XnWuDINknpc4AdVXVvVd0H7ADO6GdjJEmSJI0tv8CWpAGY1xxFSVYDTweuAyba5KIAd9ENTYMuiXRHz267W9ls5QeeYxPdCzkTExNMTU3NJ8SHbF67b8byicNn3wYs+HyLtXfv3qGd+2CMa+5GMSYwrvka1bgkSVrp2mePO9vyV5P0foE92aptA6aAl9HzBTZwbZLpL7AnaV9gAySZ/gL73QNrjCSNsDknipI8Bvgz4Deq6ivdVESdqqok1Y+AqmorsBVg3bp1NTk5uaDjbNxy1Yzlm9fu4+KbZm/2rvMWdr7FmpqaYqFtXUrGNXejGBMY13yNalySJOm7BvEFtiStVHNKFCV5GF2S6J1V9b5WfHeSY6rqzpaZv6eV7wGO79n9uFa2h+9m+qfLpxYeuiRJkqSVZlBfYPdrpMOhRjTMZlyvcPbq7f35fOzP52N/w3o+5nLXswCXALdW1et7Nm0HNtDd3ngDcGVP+UuSXE437veBlkz6MPC7PeN/Twde3p9mSJIkSRp3g/wCu18jHd78zisPOqJhNsMa6bDUvHp7fz4f+/P52N+wno+53PXsmcAvAc9KcmN7nEmXIHp2ktuAn23rAFcDtwM7gT8Gfg2gjQF+NXB9e7xqelywJEmSJB3MHL7Ahu/9AvtF7e5np9K+wAY+DJye5Kj2JfbprUySxByuKKqqvwQyy+bTZqhfwAWzHOtS4NL5BChJkiRJfPcL7JuS3NjKXkH3hfUVSc4Hvgic07ZdDZxJ9wX2g8CLofsCO8n0F9jgF9iStJ/5XwMpSZIkSQPmF9iSNBhzGXomSZIkSZKkFcBEkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSJEmSJElqTBRJkiRJkiQJMFEkSZIkSZKkxkSRJEmSJEmSABNFkiRJkiRJakwUSZIkSZIkCTBRJEmSJEmSpMZEkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSJEmSJElqTBRJkiRJkiQJMFEkSZIkSZKkxkSRJEmSJEmSABNFkiRJkiRJakwUSZIkSZIkCTBRJEmSJEmSpMZEkSRJkiRJkgATRZIkSZIkSWpMFEmSJEmSJAkwUSRJkiRJkqTGRJEkSZIkSZIAE0WSpCFLsivJTUluTPLJVvb4JDuS3NZ+HtXKk+RNSXYm+UySZww3ekmSJGm8mCiSJI2Cn6mqk6pqXVvfAlxTVWuAa9o6wHOBNe2xCXjbwCOVJEmSxpiJIknSKFoPbGvL24Cze8ovq861wJFJjhlCfJIkSdJYWjXsACRJK14BH0lSwB9V1VZgoqrubNvvAiba8rHAHT377m5ld/aUkWQT3RVHTExMMDU1taDANq/dt6D95mri8P6cY6HtG5a9e/cuu5gXayW2GWy3JEnL0SETRUkuBX4OuKeqntrKHg+8B1gN7ALOqar7kgR4I3Am8CCwsao+1fbZALyyHfY1VbUNSZLgp6pqT5IfAHYk+dvejVVVLYk0Zy3ZtBVg3bp1NTk5uaDANm65akH7zdXmtfu4+KbFf2ez67zJxQczQFNTUyz0d7JcrcQ2g+2WJGk5msvQs7cDZxxQNq+5I1pi6ULgFOBk4MLpiUklSStbVe1pP+8B3k/XT9w9PaSs/bynVd8DHN+z+3GtTJI05pJcmuSeJJ/tKZv3zQ+SbGj1b2tfZkuSehwyUVRVnwDuPaB4vnNHPAfYUVX3VtV9wA6+N/kkSVphkjw6yRHTy8DpwGeB7cD0m/cNwJVteTvwovYB4FTggZ4hapKk8fZ2/AJbkpbcQq93n+/cEbOVS5JWtgng/d3IZVYB76qqDyW5HrgiyfnAF4FzWv2r6YY376Qb4vziwYcsSRqGqvpEktUHFK8HJtvyNmAKeBk9X2AD1yaZ/gJ7kvYFNkCS6S+w373U8UvScrHoiREWMnfEwSz1BKSHmjh0WBMPjuqkh8Y1d6MYExjXfI1qXOOqqm4HnjZD+ZeB02YoL+CCAYQmSVoeluwL7H59LlnojQvG9f2I77X25/OxP5+P/Q3r+VhooujuJMdU1Z1znDtiD9/N9E+XT8104KWegPRQE4cOa0LQUZ300LjmbhRjAuOar1GNS5IkHVy/v8Du1+eSN7/zygXduGC53ahgrnyvtT+fj/35fOxvWM/HXCaznsl85474MHB6kqPaGODTW5kkSZIkLdR8b37gTREk6RAOmShK8m7gr4AfSbK7zRdxEfDsJLcBP9vWoZs74na6uSP+GPg1gDYG+NXA9e3xqulxwZIkSZK0QH6BLUl9dshrIKvqhbNsmtfcEVV1KXDpvKKTJEmSJB76AnsSODrJbrq7l13EPG5+UFX3Jpn+Ahv8AluSvseiJ7OWJEmSpKXmF9iSNBgmiiRJWuZWz3IDh0PZddFZfY5EkiRJy91CJ7OWJEmSJEnSmDFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkwESRJEmSJEmSGhNFkiRJkiRJAkwUSZIkSZIkqTFRJEmSJEmSJMBEkSRJkiRJkhoTRZIkSZIkSQJMFEmSJEmSJKkxUSRJkiRJkiTARJEkSZIkSZIaE0WSJEmSJEkCTBRJkiRJkiSpMVEkSZIkSZIkAFYNOwBJkjQcq7dctaD9dl10Vp8jkSRJ0qjwiiJJkiRJkiQBJookSZIkSZLUmCiSJEmSJEkSYKJIkiRJkiRJjYkiSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEgCrhh2AJElaXlZvuWpB++266Kw+RyJJkqR+84oiSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEjCEOYqSnAG8ETgM+JOqumjQMRyM8y5I0ugb9b5EM5vuYzev3cfGefS39rGSloJ9iSTNbKBXFCU5DHgr8FzgROCFSU4cZAySpOXNvkSStFj2JZI0u0FfUXQysLOqbgdIcjmwHrhlwHH0nVciSdLAjG1fopkttI8dJfO9imqufB8hLZh9iSTNYtCJomOBO3rWdwOn9FZIsgnY1Fb3JvlcPwP4D3A08KV+HnMx8rqHFkcqrh7GNXejGBMY13wtdVxPXMJjrxRD70v6ZdT6pEFZie1eqjb3vI8YVSvud93Yl4y+QfYlC/p7WAb/3wu1Ul8XZuPzsT+fj/0t5fMxa18y8DmKDqWqtgJbl+r4ST5ZVeuW6vgLZVzzM4pxjWJMYFzzNapxaX6Wui/pl5X697YS270S2wy2e9hxaHH61Zf497A/n4/9+Xzsz+djf8N6PgZ917M9wPE968e1MkmS5sq+RJK0WPYlkjSLQSeKrgfWJDkhycOBc4HtA45BkrS82ZdIkhbLvkSSZjHQoWdVtS/JS4AP092G8tKqunmQMTC6QxGMa35GMa5RjAmMa75GNS41I9KX9MtK/Xtbie1eiW0G260RNeC+xL+H/fl87M/nY38+H/sbyvORqhrGeSVJkiRJkjRiBj30TJIkSZIkSSPKRJEkSZIkSZKAMU4UJTkjyeeS7EyyZYbtj0jynrb9uiSrRySun07yqST7kjx/EDHNMa7fTHJLks8kuSbJE0cgpl9NclOSG5P8ZZITlzqmucTVU+9fJ6kkA7md4Ryer41J/rE9Xzcm+ZVRiKvVOaf9fd2c5F2jEFeSN/Q8V3+X5P5BxKXxNKp90lIa1f5uqY1ifzoIo9pnL7VRfU+g4ViJr/UHM6rvTYchyaVJ7kny2Vm2J8mb2nP1mSTPGHSMgzSH52MyyQM9fxv/ZdAxDlKS45N8vOfz0K/PUGewfyNVNXYPugnpPg/8M+DhwN8AJx5Q59eAP2zL5wLvGZG4VgM/DlwGPH+Enq+fAR7Vlv/9Uj9fc4zpsT3LzwM+NArPVat3BPAJ4Fpg3SjEBWwE3jKIv6l5xrUG+DRwVFv/gVGI64D6L6Wb5HJgz52P8XmMap80Am0eeH83Iu0eaH86Qu0eeJ89Cu1u9Qb6nsDH6P49jNtrfR+ej4G/Nx3i8/HTwDOAz86y/Uzgg0CAU4Hrhh3zkJ+PSeADw45zgM/HMcAz2vIRwN/N8P8y0L+Rcb2i6GRgZ1XdXlXfBC4H1h9QZz2wrS2/FzgtSYYdV1XtqqrPAN9Z4ljmG9fHq+rBtnotcNwIxPSVntVHA4OYmX0uf1sArwZeB3x9ADHNJ65Bm0tc/xZ4a1XdB1BV94xIXL1eCLx7AHFpPI1qn7SURrW/W2qj2J8Owqj22UttVN8TaDhW4mv9wYzqe9OhqKpPAPcepMp64LLqXAscmeSYwUQ3eHN4PlaUqrqzqj7Vlr8K3Aoce0C1gf6NjGui6Fjgjp713XzvE/1QnaraBzwAPGEE4hqG+cZ1Pl02cynNKaYkFyT5PPDfgP+wxDHNKa52GeDxVXXVAOKZc1zNv26XKr43yfEjEteTgScn+T9Jrk1yxojEBUAbFnIC8LEBxKXxNKp90lIa1f5uqY1ifzoIo9pnL7VRfU+g4ViJr/UHM6rvTUfVSu03D+Ynk/xNkg8mecqwgxmUNiT16cB1B2wa6N/IuCaKtESS/CKwDvjvw44FoKreWlX/HHgZ8Mphx5Pk+4DXA5uHHcsM/hewuqp+HNjBd7/RGrZVdMPPJumu3PnjJEcOM6ADnAu8t6q+PexAJI2PUetPB2HU+uylNuLvCaRRMKrvTTV8nwKeWFVPA94M/PlwwxmMJI8B/gz4jQOuxB24cU0U7QF6M9LHtbIZ6yRZBTwO+PIIxDUMc4oryc8Cvw08r6q+MQox9bgcOHspA2oOFdcRwFOBqSS76MaPbh/A5JWHfL6q6ss9v7c/AX5iiWOaU1x02fDtVfWtqvoC3ZjcNSMQ17RzcdiZFmdU+6SlNKr93VIbxf50EEa1z15qo/qeQMOxEl/rD2ZU35uOqpXab86oqr5SVXvb8tXAw5IcPeSwllSSh9Elid5ZVe+bocpA/0bGNVF0PbAmyQlJHk73QW/7AXW2Axva8vOBj1WbJWrIcQ3DIeNK8nTgj+je1A5iDpm5xNSbTDgLuG3YcVXVA1V1dFWtrqrVdPNPPK+qPjnMuAAOGMP6PLqxr0ttLn/zf053NRGtA3gycPsIxEWSHwWO+v+3d4cuVkRRHMe/P9BmNJj8LxTBbnOT4RVFo7DYLBbRKpi3aDEoGIQNwv4FFoMgatkkglEsGnxyDDNicpkNd+Yy+/3Ex/A479775pw5w9wB3jSOR+vWa05qqdd811qP+XQOvebs1nqtCbSMk3iuP0qvtWmv9oEb45utLgHfq+rr0kEtJcm5v/t3JbnI0LdYa1OV8bc+AT5V1eP/HDbrGjnV6ouXVFXbJLvAAcOO+0+r6kOSh8DbqtpnmIhnSQ4ZNtLa9BBXkgvAK4aL06tJHlRV02cyJ47XI+AM8HL8z36uqp2FY9od78r+Ar7xL/E2MzGu2U2M606SHWDLsOZvdhLXAXAlyUfgN3C3qpomgmPM4wZ4seIiTjPoNSe11Gu+a63HfDqHXnN2a73WBFrGSTzXH6XX2nQpSZ4z3Bg9m+QLcB84DVBVe8BrhrdaHQI/gFvLRDqPCeNxDbidZAv8BDYrr8cvA9eB90nejZ/dA87DMmsk6x5vSZIkSZIkTbXWR88kSZIkSZJ0TDaKJEmSJEmSBNgokiRJkiRJ0shGkSRJkiRJkgAbRZIkSZIkSRrZKJIkSZIkSRJgo0iSJEmSJEmjP29gfUu/USKTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
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